{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a5899a2",
   "metadata": {},
   "source": [
    "# 把movie文件夹下的imdb_processed.csv读取，分为训练集和测试集，训练集和测试集的特征，都单独写入一个文件，通过subword-nmt命令进行bpe分词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2d80123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集已保存至 'movie/bpe/train_full.csv'，共 40000 条数据\n",
      "测试集已保存至 'movie/bpe/test_full.csv'，共 10000 条数据\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 读取数据\n",
    "df = pd.read_csv('movie/imdb_processed.csv')\n",
    "\n",
    "# 分割训练集和测试集\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建目录（如果不存在）\n",
    "os.makedirs('movie/bpe', exist_ok=True)\n",
    "\n",
    "# 将训练集和测试集的文本写入文件\n",
    "train_df['processed'].to_csv('movie/bpe/train.txt', index=False, header=False)\n",
    "test_df['processed'].to_csv('movie/bpe/test.txt', index=False, header=False)\n",
    "# 将训练集和测试集完整数据写入文件\n",
    "train_df.to_csv('movie/bpe/train_full.csv', index=False)\n",
    "test_df.to_csv('movie/bpe/test_full.csv', index=False)\n",
    "\n",
    "print(f\"训练集已保存至 'movie/bpe/train_full.csv'，共 {len(train_df)} 条数据\")\n",
    "print(f\"测试集已保存至 'movie/bpe/test_full.csv'，共 {len(test_df)} 条数据\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cced9810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/d/BaiduSyncdisk/pytorch/chapter_7_AI\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2678c093",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 使用subword-nmt进行BPE分词\n",
    "# 1. 学习BPE编码（在训练集上）\n",
    "# 学习bpe分词(很慢,学一次就好)\n",
    "# -i 选择学习的文件\n",
    "# -o 核心输出文件,分词需要用到imdb_bpe_code,生成的 imdb_bpe_code 文件包含了学习到的 BPE 操作规则。这些规则用于将单词分割成子词单元\n",
    "# --write-vocabulary 字典输出文件，imdb_bpe_vocab 文件包含了根据 BPE 规则生成的词汇表，列出了所有子词单元及其频率\n",
    "# -s 词表大小\n",
    "!subword-nmt learn-joint-bpe-and-vocab -i ./movie/bpe/train.txt -o ./movie/bpe/imdb_bpe_code --write-vocabulary ./movie/bpe/imdb_bpe_vocab -s 8000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb130500",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 应用BPE编码到训练集和测试集\n",
    "!subword-nmt apply-bpe -c movie/bpe/imdb_bpe_code -i movie/bpe/train.txt -o movie/bpe/train.bpe.txt\n",
    "!subword-nmt apply-bpe -c movie/bpe/imdb_bpe_code -i movie/bpe/test.txt -o movie/bpe/test.bpe.txt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "672808d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 3. 获取词汇表\n",
    "# !subword-nmt get-vocab < movie/bpe/imdb_bpe_vocab > movie/bpe/vocab.txt\n",
    "\n",
    "# print(\"BPE处理完成！\")\n",
    "# print(f\"训练集大小: {len(train_df)}\")\n",
    "# print(f\"测试集大小: {len(test_df)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "afee52b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "词典大小: 8105\n",
      "前10个词条: [('[PAD]', 0), ('[BOS]', 1), ('[UNK]', 2), ('[EOS]', 3), ('.', 4), (',', 5), ('I', 6), ('-', 7), ('movie', 8), ('film', 9)]\n"
     ]
    }
   ],
   "source": [
    "# 读取BPE词汇表并构建词典\n",
    "vocab = {}\n",
    "special_tokens = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "# 首先添加特殊标记\n",
    "vocab.update(special_tokens)\n",
    "\n",
    "# 读取BPE词汇表\n",
    "j=len(special_tokens)\n",
    "with open('movie/bpe/imdb_bpe_vocab', 'r', encoding='utf-8') as f:\n",
    "    for i, line in enumerate(f):\n",
    "        token, count = line.strip().split(' ')\n",
    "        if token not in vocab:  # 避免与特殊标记冲突\n",
    "            vocab[token] = j\n",
    "            j+=1\n",
    "\n",
    "print(f\"词典大小: {len(vocab)}\")\n",
    "print(f\"前10个词条: {list(vocab.items())[:10]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9d3ab3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8101 movie/bpe/imdb_bpe_vocab\n"
     ]
    }
   ],
   "source": [
    "!wc -l movie/bpe/imdb_bpe_vocab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a747c92",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a7ead6",
   "metadata": {},
   "source": [
    "# Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6291049d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "class Tokenizer:\n",
    "    def __init__(self, vocab):\n",
    "        \"\"\"\n",
    "        初始化分词器\n",
    "        \n",
    "        参数:\n",
    "            vocab: 词汇表字典，将token映射到ID\n",
    "        \"\"\"\n",
    "        self.vocab = vocab #子词到id的映射\n",
    "        self.id_to_token = {v: k for k, v in vocab.items()} #id到子词的映射\n",
    "        self.pad_id = vocab[\"[PAD]\"] #填充token的id\n",
    "        self.bos_id = vocab[\"[BOS]\"]\n",
    "        self.eos_id = vocab[\"[EOS]\"]\n",
    "        self.unk_id = vocab[\"[UNK]\"]\n",
    "    \n",
    "    def encode(self, text_list, max_length=None, padding_first=False):\n",
    "        \"\"\"\n",
    "        将文本编码为ID序列\n",
    "        \n",
    "        参数:\n",
    "            text_list: 输入文本列表，可以是单个句子或多个句子\n",
    "            max_length: 最大序列长度，如果指定则会进行截断或填充\n",
    "            padding_first: 如果为True，则在序列前面填充，否则在后面填充\n",
    "            \n",
    "        返回:\n",
    "            token_ids: ID序列张量\n",
    "        \"\"\"\n",
    "            \n",
    "        batch_max_length = 2 + max([len(text) for text in text_list]) # 计算当前批次最大序列长度\n",
    "        if batch_max_length <max_length:\n",
    "            max_length = batch_max_length\n",
    "            \n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 转换为ID\n",
    "            indices = [self.bos_id] + [self.vocab.get(token, self.unk_id) for token in text[:max_length-2]] + [self.eos_id]\n",
    "            \n",
    "            # 处理填充\n",
    "            if padding_first:  # padding加在前面\n",
    "                indices = [self.pad_id] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加在后面\n",
    "                indices = indices + [self.pad_id] * (max_length - len(indices))\n",
    "                \n",
    "            indices_list.append(indices)\n",
    "        \n",
    "        return torch.tensor(indices_list)\n",
    "    \n",
    "    def decode(self, token_ids):\n",
    "        \"\"\"\n",
    "        将ID序列解码为文本\n",
    "        \n",
    "        参数:\n",
    "            token_ids: ID序列\n",
    "            \n",
    "        返回:\n",
    "            text: 解码后的文本\n",
    "        \"\"\"\n",
    "        if isinstance(token_ids, torch.Tensor):\n",
    "            token_ids = token_ids.tolist()\n",
    "            \n",
    "        if isinstance(token_ids[0], list):\n",
    "            return [self.decode(ids) for ids in token_ids]\n",
    "            \n",
    "        tokens = []\n",
    "        for id in token_ids:\n",
    "            if id in [self.pad_id, self.bos_id, self.eos_id]:\n",
    "                continue\n",
    "            \n",
    "            token = self.id_to_token.get(id, \"[UNK]\")\n",
    "            tokens.append(token)\n",
    "        \n",
    "        # 合并子词\n",
    "        text = \"\"\n",
    "        for token in tokens:\n",
    "            if token.endswith(\"@@\"):\n",
    "                text += token[:-2]  # 移除@@后缀\n",
    "            else:\n",
    "                text += token + \" \"\n",
    "        \n",
    "        return text.strip()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4c3d2c",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e865a3e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/d/BaiduSyncdisk/pytorch/chapter_7_AI\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f79b7bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "import pandas as pd\n",
    "\n",
    "class IMDBDataset(Dataset):\n",
    "    \"\"\"\n",
    "    IMDB数据集类，用于加载和处理IMDB电影评论数据\n",
    "    \"\"\"\n",
    "    def __init__(self, bpe_file, vocab, max_length=512, mode='train'):\n",
    "        \"\"\"\n",
    "        初始化数据集\n",
    "        \n",
    "        参数:\n",
    "            bpe_file: 包含BPE处理后的文本文件路径\n",
    "            csv_file: 包含电影评论标签的CSV文件路径\n",
    "            vocab: 词汇表字典\n",
    "            max_length: 序列最大长度\n",
    "        \"\"\"\n",
    "        # 读取标签数据\n",
    "        # 只读取前4万行数据\n",
    "        # 根据文件类型决定读取的数据行\n",
    "        if mode == 'train':\n",
    "            # 训练集读取前4万行\n",
    "            self.labels = pd.read_csv('movie/bpe/train_full.csv')['label'].values\n",
    "        else:\n",
    "            # 测试集读取最后1万行\n",
    "            df = pd.read_csv('movie/bpe/test_full.csv')\n",
    "            self.labels = df.loc[:,'label'].values\n",
    "        \n",
    "        # 读取BPE处理后的文本\n",
    "        self.texts = []\n",
    "        with open(bpe_file, 'r', encoding='utf-8') as f:\n",
    "            self.texts = [line.strip() for line in f]\n",
    "            \n",
    "        # 确保文本和标签数量一致\n",
    "        assert len(self.texts) == len(self.labels), \"文本和标签数量不匹配\"\n",
    "        \n",
    "        self.tokenizer = Tokenizer(vocab)\n",
    "        self.max_length = max_length\n",
    "        \n",
    "    def __len__(self):\n",
    "        \"\"\"返回数据集大小\"\"\"\n",
    "        return len(self.texts)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        \"\"\"\n",
    "        获取指定索引的样本\n",
    "        \n",
    "        参数:\n",
    "            idx: 样本索引\n",
    "            \n",
    "        返回:\n",
    "            tokens: 分词后的评论文本\n",
    "            label: 情感标签 (0表示负面, 1表示正面)\n",
    "        \"\"\"\n",
    "        # 获取评论文本和标签\n",
    "        text = self.texts[idx]\n",
    "        label = self.labels[idx]\n",
    "    \n",
    "        return text.split(), label\n",
    "\n",
    "train_ds = IMDBDataset(bpe_file='movie/bpe/train.bpe.txt',  vocab=vocab, mode='train')\n",
    "test_ds = IMDBDataset(bpe_file='movie/bpe/test.bpe.txt', vocab=vocab, mode='test')\n",
    "\n",
    "# print(train_ds[0])\n",
    "# print(test_ds[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8e3b8666",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 22343 (\\N{CJK UNIFIED IDEOGRAPH-5747}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 38271 (\\N{CJK UNIFIED IDEOGRAPH-957F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 20013 (\\N{CJK UNIFIED IDEOGRAPH-4E2D}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 20301 (\\N{CJK UNIFIED IDEOGRAPH-4F4D}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Local\\Temp\\ipykernel_27580\\1149141991.py:19: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24179 (\\N{CJK UNIFIED IDEOGRAPH-5E73}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22343 (\\N{CJK UNIFIED IDEOGRAPH-5747}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38271 (\\N{CJK UNIFIED IDEOGRAPH-957F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20013 (\\N{CJK UNIFIED IDEOGRAPH-4E2D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20301 (\\N{CJK UNIFIED IDEOGRAPH-4F4D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\41507\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最短样本长度: 6\n",
      "最长样本长度: 2749\n",
      "平均样本长度: 199.31\n",
      "样本长度中位数: 147.0\n",
      "样本长度标准差: 155.51\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 统计训练集中样本的长度分布\n",
    "sample_lengths = [len(train_ds[i][0]) for i in range(len(train_ds))]\n",
    "\n",
    "# 绘制直方图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(sample_lengths, bins=50, alpha=0.7, color='blue')\n",
    "plt.xlabel('Sample Length')\n",
    "plt.ylabel('Sample Count')\n",
    "plt.title('Training Set Sample Length Distribution')\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "\n",
    "# 添加一些统计信息\n",
    "plt.axvline(x=np.mean(sample_lengths), color='r', linestyle='--', label=f'平均长度: {np.mean(sample_lengths):.2f}')\n",
    "plt.axvline(x=np.median(sample_lengths), color='g', linestyle='-.', label=f'中位数长度: {np.median(sample_lengths):.2f}')\n",
    "plt.axvline(x=np.percentile(sample_lengths, 95), color='orange', linestyle=':', label=f'95%分位数: {np.percentile(sample_lengths, 95):.2f}')\n",
    "\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 打印一些统计信息\n",
    "print(f\"最短样本长度: {min(sample_lengths)}\")\n",
    "print(f\"最长样本长度: {max(sample_lengths)}\")\n",
    "print(f\"平均样本长度: {np.mean(sample_lengths):.2f}\")\n",
    "print(f\"样本长度中位数: {np.median(sample_lengths)}\")\n",
    "print(f\"样本长度标准差: {np.std(sample_lengths):.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e718618",
   "metadata": {},
   "source": [
    "# DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e17a21c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集DataLoader批次数量: 1250\n",
      "验证集DataLoader批次数量: 313\n",
      "批次中的token_ids形状: torch.Size([32, 500])\n",
      "批次中的标签形状: torch.Size([32, 1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[   0,    0,    0,  ...,  991, 1703,    3],\n",
       "        [   0,    0,    0,  ..., 1206,   14,    3],\n",
       "        [   0,    0,    0,  ...,  891,   14,    3],\n",
       "        ...,\n",
       "        [   0,    0,    0,  ...,  512,   14,    3],\n",
       "        [   0,    0,    0,  ...,   18,   14,    3],\n",
       "        [   1,   29,  791,  ..., 4086,  199,    3]], dtype=torch.int32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "\n",
    "tokenizer = Tokenizer(vocab)\n",
    "\n",
    "def collate_fct(batch):\n",
    "    \"\"\"\n",
    "    把字符串列表转化为tensor\n",
    "    :param batch:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    text_list = [item[0] for item in batch] #batch中每个item的第一个元素是text,是输入，类型为list\n",
    "    label_list = [item[1] for item in batch] #batch中每个item的第二个元素是label,是输出，类型为int\n",
    "    # 这里使用 padding first\n",
    "    text_list = tokenizer.encode(text_list, max_length=500, padding_first=True).to(dtype=torch.int)\n",
    "    return text_list, torch.tensor(label_list).reshape(-1, 1).to(dtype=torch.float)\n",
    "\n",
    "def create_dataloaders(train_ds,test_ds,batch_size=4):\n",
    "    \"\"\"\n",
    "    创建训练集和验证集的DataLoader\n",
    "    \n",
    "    参数:\n",
    "        train_texts: 训练集文本\n",
    "        train_labels: 训练集标签\n",
    "        val_texts: 验证集文本\n",
    "        val_labels: 验证集标签\n",
    "        tokenizer: 分词器\n",
    "        batch_size: 批次大小\n",
    "        \n",
    "    返回:\n",
    "        train_dataloader: 训练集DataLoader\n",
    "        val_dataloader: 验证集DataLoader\n",
    "    \"\"\"\n",
    "    \n",
    "    # 创建DataLoader\n",
    "    train_dataloader = DataLoader(\n",
    "        train_ds, \n",
    "        batch_size=batch_size, \n",
    "        shuffle=True, \n",
    "        collate_fn=collate_fct\n",
    "    )\n",
    "    \n",
    "    val_dataloader = DataLoader(\n",
    "        test_ds, \n",
    "        batch_size=batch_size, \n",
    "        shuffle=False, \n",
    "        collate_fn=collate_fct\n",
    "    )\n",
    "    \n",
    "    return train_dataloader, val_dataloader\n",
    "\n",
    "# 调用create_dataloaders函数初始化训练集和测试集的DataLoader\n",
    "batch_size = 32 # 设置批次大小\n",
    "train_dataloader, val_dataloader = create_dataloaders(\n",
    "    train_ds=train_ds,\n",
    "    test_ds=test_ds,\n",
    "    batch_size=batch_size\n",
    ")\n",
    "\n",
    "print(f\"训练集DataLoader批次数量: {len(train_dataloader)}\")\n",
    "print(f\"验证集DataLoader批次数量: {len(val_dataloader)}\")\n",
    "\n",
    "# 查看一个批次的数据样例\n",
    "sample_batch = next(iter(train_dataloader))\n",
    "token_ids, labels = sample_batch\n",
    "print(f\"批次中的token_ids形状: {token_ids.shape}\")\n",
    "print(f\"批次中的标签形状: {labels.shape}\")\n",
    "\n",
    "token_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81586740",
   "metadata": {},
   "source": [
    "# 搭建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7e28fc1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型结构:\n",
      "SentimentClassifier(\n",
      "  (embedding): Embedding(8105, 16)\n",
      "  (lstm): LSTM(16, 128, batch_first=True)\n",
      "  (fc1): Linear(in_features=128, out_features=128, bias=True)\n",
      "  (fc): Linear(in_features=128, out_features=1, bias=True)\n",
      "  (dropout): Dropout(p=0.3, inplace=False)\n",
      ")\n",
      "单层单向lstm模型参数数量: 221,073\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class SentimentClassifier(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim=16, hidden_dim=128, output_dim=1, \n",
    "                 lstm_layers=1, bidirectional=False, dropout_rate=0.3):\n",
    "        super().__init__()\n",
    "        \n",
    "        # 嵌入层\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        \n",
    "        # 设置lstm层\n",
    "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, \n",
    "                         num_layers=lstm_layers, # 层数\n",
    "                         bidirectional=bidirectional, # 是否双向\n",
    "                         batch_first=True,\n",
    "                         dropout=dropout_rate if lstm_layers > 1 else 0)\n",
    "        \n",
    "        # 确定输出维度\n",
    "        fc_input_dim = hidden_dim * 2 if bidirectional else hidden_dim\n",
    "        \n",
    "        # 增加一个fc层，输入是fc_input_dim，输出是hidden_dim\n",
    "        self.fc1 = nn.Linear(fc_input_dim, hidden_dim)\n",
    "        \n",
    "        # 全连接层\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "        # Dropout层，防止过拟合\n",
    "        self.dropout = nn.Dropout(dropout_rate)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x形状: [batch_size, seq_len]\n",
    "        \n",
    "        # 通过嵌入层\n",
    "        embedded = self.embedding(x)  # [batch_size, seq_len, embedding_dim]\n",
    "        \n",
    "        # 通过lstm层\n",
    "        outputs, hidden = self.lstm(embedded)\n",
    "        \n",
    "        # print(f'outputs.shape: {outputs.shape}')\n",
    "        # print(f'hidden.shape: {hidden.shape}')\n",
    "        \n",
    "        # 取最后一个时间步的输出 (这也是为什么要设置padding_first=True的原因)\n",
    "        x = outputs[:, -1, :]\n",
    "        # print(f'hidden: {hidden}') #第一个张量是h, 第二个张量是c\n",
    "        # print(f'len(hidden): {len(hidden)}')\n",
    "        # #输出h，c的形状\n",
    "        # print(f'h是 hidden[0].shape: {hidden[0].shape}')\n",
    "        # print(f'c是 hidden[1].shape: {hidden[1].shape}')\n",
    "        # # # 打印一下形状，观察数据\n",
    "        # print(f'x.shape: {x.shape}')\n",
    "\n",
    "        # hidden_reshaped = hidden[0].squeeze(0) \n",
    "     \n",
    "        # # # 判断两个张量是否相等\n",
    "        # is_equal = torch.allclose(x, hidden_reshaped)\n",
    "        # print(f'是否相等: {is_equal}')\n",
    "        \n",
    "        # 应用dropout\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.fc(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "# 初始化模型\n",
    "model = SentimentClassifier(vocab_size=len(vocab), lstm_layers=1, bidirectional=False)\n",
    "print(f\"模型结构:\\n{model}\")\n",
    "\n",
    "# 打印模型参数数量\n",
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f\"单层单向lstm模型参数数量: {count_parameters(model):,}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f6f803c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cpu\n",
      "输入形状: torch.Size([32, 500])\n",
      "输出形状: torch.Size([32, 1])\n"
     ]
    }
   ],
   "source": [
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"使用设备: {device}\")\n",
    "# 验证模型的前向计算\n",
    "# 从训练数据中获取一个批次的样本\n",
    "sample_batch, sample_labels = next(iter(train_dataloader))\n",
    "print(f\"输入形状: {sample_batch.shape}\")\n",
    "\n",
    "# 将样本移动到设备上\n",
    "sample_batch = sample_batch.to(device)\n",
    "\n",
    "# 进行前向计算\n",
    "with torch.no_grad():\n",
    "    outputs = model(sample_batch)\n",
    "    print(f\"输出形状: {outputs.shape}\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc05baa",
   "metadata": {},
   "source": [
    "# 训练，画图，评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "175bd35f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型结构:\n",
      "SentimentClassifier(\n",
      "  (embedding): Embedding(8105, 16)\n",
      "  (lstm): LSTM(16, 128, batch_first=True, bidirectional=True)\n",
      "  (fc1): Linear(in_features=256, out_features=128, bias=True)\n",
      "  (fc): Linear(in_features=128, out_features=1, bias=True)\n",
      "  (dropout): Dropout(p=0.3, inplace=False)\n",
      ")\n",
      "训练开始，共25000步\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05ec7bd8c3aa4eb78d3bf918e0e9b044",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "早停触发! 最佳验证准确率(如果是回归，这里是损失): 76.6900\n",
      "早停: 在2300 步\n"
     ]
    }
   ],
   "source": [
    "from wangdao_deeplearning_train import train_two_classification_model, evaluate_two_classification_model,plot_learning_curves,EarlyStopping,ModelSaver\n",
    "\n",
    "model = SentimentClassifier(vocab_size=len(vocab), lstm_layers=1, bidirectional=True)\n",
    "print(f\"模型结构:\\n{model}\")\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.BCEWithLogitsLoss() #WithLogitsLoss代表的含义是：把输出结果通过sigmoid函数，然后计算损失\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "# 将模型移动到设备上\n",
    "model = model.to(device)\n",
    "\n",
    "\n",
    "# 训练参数\n",
    "num_epochs = 20\n",
    "eval_step = 100\n",
    "\n",
    "# 训练模型\n",
    "# 创建早停和模型保存器\n",
    "early_stopping = EarlyStopping(patience=5, delta=0.001)\n",
    "model_saver = ModelSaver(save_dir='weights')\n",
    "\n",
    "model, record_dict = train_two_classification_model(\n",
    "    model=model,\n",
    "    train_loader=train_dataloader,\n",
    "    val_loader=val_dataloader,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer,\n",
    "    device=device,\n",
    "    num_epochs=num_epochs,\n",
    "    eval_step=eval_step,\n",
    "    early_stopping=early_stopping,\n",
    "    model_saver=model_saver\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d91eae27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制学习曲线\n",
    "plot_learning_curves(record_dict,sample_step=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3075fd61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率: 75.01%, 测试集损失: 0.5158\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 在测试集上评估最终模型\n",
    "test_acc, test_loss = evaluate_two_classification_model(model, val_dataloader, device, criterion)\n",
    "print(f\"测试集准确率: {test_acc:.2f}%, 测试集损失: {test_loss:.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
